Neural network based determination of gaze direction using spatial models

ABSTRACT

Systems and methods for determining the gaze direction of a subject and projecting this gaze direction onto specific regions of an arbitrary three-dimensional geometry. In an exemplary embodiment, gaze direction may be determined by a regression-based machine learning model. The determined gaze direction is then projected onto a three-dimensional map or set of surfaces that may represent any desired object or system. Maps may represent any three-dimensional layout or geometry, whether actual or virtual. Gaze vectors can thus be used to determine the object of gaze within any environment. Systems can also readily and efficiently adapt for use in different environments by retrieving a different set of surfaces or regions for each environment.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/948,796, filed Dec. 16, 2019, the disclosure of whichis hereby incorporated by reference herein in its entirety. Thisapplication also incorporates by reference U.S. patent application Ser.No. 17/004,252, which was filed on Aug. 27, 2020, herein in itsentirety.

BACKGROUND

Recent convolutional neural networks (CNNs) have been developed toestimate gaze direction of subjects. Such CNNs can, for example,determine the direction a subject is looking from an input image of thesubject. This allows systems using such CNNs to track subject gaze andreact accordingly in real time.

Conventional gaze determination systems are not without their drawbacks,however. In particular, while conventional systems may determine gazedirection generally, they are unable to specifically pinpoint what thesubject is actually looking at. For example, while some conventionalin-vehicle gaze determination systems may determine that the driver islooking in a particular direction, e.g., straight ahead, off to oneside, or the like, such systems do not determine the particular objector item the driver is gazing at, e.g., the dashboard, the road, theradio, or the like.

Accordingly, systems and methods are described herein for conductingmachine learning based gaze analysis in more specific manner.Embodiments of the disclosure thus describe systems and methods for morespecific, efficient, and flexible determination of gaze region. In anexemplary embodiment, gaze vectors are determined by a regression-basedmachine learning model. Determined gaze vectors are then projected ontoa three-dimensional map of surfaces that may represent any desiredobject or system. Maps may represent any three-dimensional layout orgeometry. In this manner, gaze vectors can be used to determine theobject of a subject's gaze in an arbitrary environment. Furthermore,systems can be readily and efficiently generated to determine the gazeof a subject operating within any system, with any actions taken inresponse.

In one embodiment of the disclosure, a machine learning model isemployed to determine the gaze direction of a subject. The model mayhave as input features determined from image data of the subject, whichmay include relevant portions of the subject's image such as eye crops,one or more facial landmarks of the subject, and the like. Inputs mayalso include quantities determined from the subject's image, such ashead pose, confidence values, and the like. In response, the modelgenerates a gaze direction of the subject, as its output.

The system also retrieves a set of spatial regions, i.e., defined areasor volumes. These regions may be defined in any manner, to correspond toreal-world spatial objects. For instance, the set of spatial regions maycorrespond to locations and orientations of various interior surfaces ofa vehicle. The system may then determine, from the gaze direction andthe locations of the spatial regions, whether the subject's gazeintersects one or more of the spatial regions. If so, the system mayinitiate an operation in response. Any such operation is contemplated.For example, when the subject is the driver of a vehicle and the spatialregions correspond to interior surfaces of the vehicle, the system maydetermine that the driver is gazing at a surface corresponding to thevehicle entertainment console, and may take responsive actions such asactivating its interface, turning on/off displays, adjusting the volume,and the like.

The machine learning model may be any one or more models suitable fordetermining gaze direction of a subject from image data of the subject.As one example, the machine learning model may employ a regression modelto determine gaze direction as a function of its various inputs.

As above, the set of spatial regions may describe the various locationsand orientations of any set of surfaces. Accordingly, these spatialregions may describe any three-dimensional surfaces, arranged andoriented in any manner as desired. These surfaces may thus model anyreal- or virtual-world environment or object of interest, and systems ofthe disclosure may thus be used to determine the precise object orportion of his or her environment (i.e., which three-dimensionalsurface) upon which the subject is currently gazing. For instance, thesurfaces may be the three-dimensional surfaces visible from the interiorof a particular vehicle, which may include representations of thevarious windows of the vehicle as well as elements such as particularinstruments, components, or features of the vehicle such as the radio,air conditioning system, dashboard displays, and the like. In thismanner, the system may determine whether the driver's gaze is currentlyintersecting surfaces representing particular components, and takeappropriate action by initiating some operation of the vehicle. As oneexample, the system may determine that the driver is currently gazing atthe air conditioning dials, and may prompt the vehicle to respond in anynumber of ways, such as by altering its temperature settings, turningon/off the air conditioner, or the like. As another example, the systemmay determine that the driver is currently distracted or asleep, and mayinitiate an alarm alerting the driver, may initiate an emergencysteering maneuver to pull the car over to the side of the road, or mayinitiate a braking maneuver. The spatial regions may be determined inany manner, such as by selecting regions taken from a computer aideddesign (CAD) or other computer based three dimensional model of one ormore objects, by directly measuring objects, by determining locations ofpoints or regions of an object from images of that object, or via amachine learning model trained to select and determine positions andorientations of regions of an object.

As above, the machine learning model or models may have any suitableinputs for determining subject gaze direction. These inputs may include,without limitation, any one or more of facial landmark points of thesubject, head pose information of the subject, one or more eye gazedirections of the subject, one or more eye crops, or any confidencevalues associated with any of these inputs.

It is also noted that the image data used by the system may be any formof image data, whether corresponding to visible light images orotherwise, and may be received from or generated by any type of sensor.It is also noted that the use of a discrete set of spatial regionsyields a modular system which can be used in conjunction with manydifferent environments by simply adding a new set of spatial regions.That is, multiple different sets of spatial regions may be stored,corresponding to any environment desired. The system may then retrievethe appropriate set of spatial regions, and repeat the above processwith the new regions. In this manner, the system may adaptivelydetermine a subject's interactions with any desired environment.

BRIEF DESCRIPTION OF THE FIGURES

The above and other objects and advantages of the disclosure will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIGS. 1A-1C are pictures illustrating operation of a system inaccordance with embodiments of the disclosure;

FIG. 2A is a block diagram illustrating an exemplary machine learningsystem for determining gaze direction and mapping this gaze direction toregions of any three-dimensional geometry, according to embodiments ofthe disclosure;

FIG. 2B is a block diagram illustrating further details of the gazevector estimation module of FIG. 2A;

FIG. 3 is a generalized embodiment of an illustrative electroniccomputing system constructed for use according to embodiments of thedisclosure;

FIG. 4A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 4B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 4A, in accordance with someembodiments of the present disclosure;

FIG. 4C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 4A, in accordance with someembodiments of the present disclosure;

FIG. 4D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 4A, in accordancewith some embodiments of the present disclosure;

FIG. 5 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure;

FIG. 6 illustrates training and deployment of a machine learning modelof embodiments of the disclosure; and

FIG. 7 is a flowchart illustrating process steps for determining gazedirection and mapping this gaze direction to regions of anythree-dimensional geometry, in accordance with embodiments of thedisclosure.

DETAILED DESCRIPTION

In one embodiment, the disclosure relates to systems and methods fordetermining the gaze direction of a subject and projecting this gazedirection onto specific regions of an arbitrary three-dimensionalgeometry. In an exemplary embodiment, gaze direction may be determinedby a regression-based machine learning model. The determined gazedirection is then projected onto a three-dimensional map or set ofsurfaces that may represent any desired object or system. Maps mayrepresent any three-dimensional layout or geometry, whether actual orvirtual. Gaze vectors can thus be used to determine the object of gazewithin any environment. Systems can also readily and efficiently adaptfor use in different environments by retrieving a different set ofsurfaces or regions for each environment.

FIGS. 1A-1C are pictures illustrating operation of a system inaccordance with embodiments of the disclosure. In FIG. 1A, a diagram 110is an interior view of a vehicle with an occupant directing a gaze atthe entertainment console, in accordance with some embodiments of thedisclosure. The occupant 116 is seated in the driver's seat of thevehicle while momentarily directing her gaze 116 (e.g., directing hereyes) towards the entertainment console 119. An interior camera sensor112 is mounted to the roof of the interior while a microphone sensor 114is mounted within the entertainment console. The processing circuitrymay receive image data from camera sensor 112 to determine the occupantand their respective gaze at the entertainment console.

The processing circuitry may calculate a gaze vector based on the dataindicative of the eye gaze of the occupant, as described herein. In someembodiments, parallel processing circuitry may implement a machinelearning model (e.g., a neural network) to calculate the gaze vector,such as described herein. The gaze vector may be a straight line inthree-dimensional space with one intersection point at the eyes of theoccupant 117 and a second intersection point at a point on the surfaceof the entertainment console 119.

The processing circuitry may determine an intersection between the gazevector and the entertainment console 119. In particular, the processingcircuitry may retrieve from a memory a stored set of spatial coordinatesrepresenting regions in three-dimensional space that correspond tovarious surfaces in the interior of the vehicle. One of these regionsoutlines the orientation and location of the entertainment console 119within the vehicle. In this example, the processing circuitry extendsthe gaze vector from its determined origin point (e.g., thethree-dimensional location of the eyes of the driver 117) to intersectthe region corresponding to the entertainment console 119.

Upon determining the intersection between the gaze vector and theentertainment console 119, the processing circuitry may cause anoperation to be performed in the vehicle. This operation may beperformed by the one of more hardware components of the vehicle. Forexample, the vehicle may be equipped with various hardware componentscapable of providing a specific operation relating to the entertainmentconsole 119, when the intersection is determined to be positioned at theentertainment console 119.

FIG. 1B illustrates a diagram 120 showing execution of a vehicleoperation in response to a determination that the driver is gazing atthe entertainment console 119, in accordance with some embodiments ofthe disclosure. The processing circuitry, subsequent to determining thatthe driver is gazing at the entertainment console 119, transmits aninstruction to the entertainment console 119 to switch modes from“sleep” to “engaged.” The engaged mode of the entertainment consoleprovides for a screen with increased brightness 122 which providesenablement for further queries or requests from the occupant forspecific operations. For example, the entertainment console, now withbrightness and UI engaged, is responsive to receiving a particular radiostation to tune to. Consequently, the system, upon recognition of thedriver gazing at the entertainment console 119, alters the operatingmode of the entertainment console 119. In this way, the system may usegaze as an operational trigger or “wake-up-word” for multimodal systems(e.g., virtual or digital personal assistants, conversational userinterfaces, and other similar interfaces).

In some embodiments, the processing circuitry may receive, from sensorsin the vehicle, other data from the occupant. For example, an interiorcamera 112 may receive lip activity of the occupant. The lip activitymay be converted into textual information (e.g., lip activity processingmay provide textual information being uttered by the occupant). In someembodiments, the other data may be audio data received by the vehicleinterior microphone sensor 114. The processing circuitry may determine aservice operation associated with the other data. For example, theprocessing circuitry may determine the occupant states “increase trebleto level 4.” The processing circuitry will determine that acorresponding operation within the entertainment console is to increasethe treble of audio/video playback. The processing circuitry may thencause the service operation to be performed in the vehicle.

Embodiments of the disclosure contemplate processing and operation inmultiple contexts, performed for multiple actors in parallel. Forexample, interior camera 200 may detect gaze activity from an occupantof the vehicle at a pre-defined point of interest (e.g., a camera) andcorrelate the detected gaze activity with lip activity and/or audio datacorresponding to the occupant. Under such circumstances, the processingcircuitry may maintain separate context streams for each occupant basedon the determined source of gaze activity. Such individual contextstreams may be timed or semi-persistent. That is, context streams may bemaintained despite being interrupted or otherwise renderednon-contiguous by activity corresponding to other context streams ormulti-modal activity from other vehicle occupants. Where the context isdetermined to be the same, dialogue systems such as those contemplatedby the present disclosure incorporate the audio data from differentoccupants to perform the same service operation. Accordingly,embodiments of the present disclosure contemplate the combination ofmultiple modalities (e.g., vision and speech) of user input detection toimplement a dialogue system for conversational artificial intelligenceoperations, and to maintain multiple, separate contexts in a dialoguesystem within the context of a vehicle cabin. Embodiments of the presentdisclosure also contemplate applications within other settings. Forexample, vision information (such as, without limitation: any or all ofgaze/body pose detection, gaze/body pose mapping, and/or objectdetection) may be combined with speech information (such as, withoutlimitation: automated speech recognition or natural language processing)to perform machine learning-assisted operations within the context of aretail store, business office, medical facility, etc. The application ofsensor fusion combined with multi-context systems to other use cases isprovided by using modifiable/customizable use case graphs. Embodimentsof the present disclosure also contemplate feedback mechanisms to informthe subject when his or her gaze is determined to intersect a particularspatial region. For instance, visual, haptic, or other feedback may begenerated at a particular vehicle component when the system determinesthat the driver is gazing upon that component. Such feedback may, forexample, further inform the driver of actions he or she may take.

FIG. 1C illustrates a diagram 130 showing multi-context analysis of useractions, in accordance with some embodiments of the disclosure. Theprocessing circuitry, after instructing the entertainment console 119 toswitch modes from “sleep” to “engaged,” may further receive camerasensor information from the camera 112 of the occupant 116 silentlystating, or whispering, the words “shift to heads-up display.” Theprocessing circuitry provides for visual processing on the video and/orpicture frames of the silent/whispered words to determine the specificwords used. The occupant may, for instance, have mouthed or whisperedthe words as to not wake another occupant sleeping in the backseat ofthe vehicle. The system, based on the occupant's gaze direction and thesubsequent determined words, causes the user interface of theentertainment console to be projected on the windshield 132.

FIG. 2A is a block diagram illustrating an exemplary machine learningsystem for determining gaze direction and mapping this gaze direction toregions of any three-dimensional geometry. The system includes a camera200, a face detection module 210, a gaze vector estimation module 220,facial landmark detection 230 and gaze origin estimation 240 modules,and a mapping module 250. The camera 200 captures images of a subject,such as a person, whose gaze direction is to be determined. The camera200 transmits image data from captured images to the face detectionmodule 210, which detects the face of the subject in the image. The facedetection module 210 may be any software module or set of instructionscapable of locating subject faces within an image using any method orprocess, and may be similar to the face detection module 210 of FIG. 2Aas described above. The system of FIG. 2A may be implemented on, andexecuted by, any computing device, such as computing device 300.

Faces detected by this face detection module 210 may be cropped, withcropped face images transmitted to the gaze vector estimation module220. Face crops may be determined by locating the subject's face in theimage from the camera 200, and cropping the image accordingly. Facelocation can be carried out in any manner, such as by known computervision-based face detection processes including any of the abovenon-neural network based techniques, neural network-based facerecognition methods, and the like.

The gaze vector estimation module 220 may implement any one or moremachine learning models capable of determining a subject's gazedirection from an input image of the subject's face. In one exemplaryembodiment, the gaze vector estimation module 220 implements aregression model that estimates direction vector values from inputgaze-related variables as further described below. The gaze vectorestimation module 220 may implement any suitable regression model ormodels, such as a DNN-based linear regression model, statisticalregression model, gradient boosting model, or the like, that may beconfigured to determine gaze vector from any input variables.

The input face crop is also input to a facial landmarks detection module230 which determines facial landmarks from the input image of thesubject's face. The facial landmarks module 230 may implement anymachine learning network, e.g., any one or more machine learning models,capable of determining facial landmarks from an input image of a face.Module 230 may include machine learning models built according toholistic methods to represent global facial appearance and shapeinformation, models built according to constrained local model methodsthat build local appearance models in addition to utilizing global shapemodels, generative networks, CNNs, and regression-based models thatdetermine landmark positions as functions of facial shape and appearanceinformation. Many such models are known, and embodiments of thedisclosure contemplate use of any one or more such models, or any othersuitable models or methods, to determine facial landmarks from an inputimage of a face. Models may be constructed using any architecture andmethods suitable for determining facial landmarks from input images offaces. For example, facial landmarks networks based on CNNs may bestructured using any convolution kernels and pooling layers suitable forextracting features of a face for determination of correspondinglandmark points.

The facial landmarks output by the facial landmarks detection module 230are then transmitted to the gaze origin estimation module 240, whichdetermines the origin point of the gaze direction vector therefrom. Thegaze origin estimation module 240 may implement any machine learningnetwork, e.g., any one or more machine learning models capable ofdetermining gaze origin points from an input set of facial landmarks.Such networks may include CNNs, classification models, regressionmodels, or the like.

The estimated gaze vector and its origin point are then input to themapping module 250, which determines a three-dimensional region thesubject is looking at, by mapping the gaze vector from its origin pointonto a set of three-dimensional regions. The mapping module 250 storesthe set of three-dimensional regions, e.g., in storage 408, whichdescribe a set of surfaces in three dimensions, and implements a mappingroutine that projects the determined gaze vector from its origin pointto determine whether it intersects one of the surfaces. The intersectedsurface, if any, is then output as the output gaze region, or thethree-dimensional surface at which the subject is currently looking.Data describing the set of three-dimensional regions may be input to,and stored in or accessible by, the mapping module 250. In this manner,any set of three-dimensional regions describing any one or more objectscan be input to the mapping module 250, and mapping module 250 maydetermine the intersection of projected gaze vectors with any storedthree-dimensional regions. This allows the system of FIG. 2A todetermine gaze direction relative to any three-dimensional regionsrepresenting any object or objects. Furthermore, the system need notre-train its machine learning models for each different object. Rather,a new set of three-dimensional regions may simply be made available tothe mapping module 250, and module 250 may determine intersections ofgaze direction with these new three-dimensional regions withoutretraining of its machine learning models.

The three-dimensional regions may be any three-dimensionalrepresentations determined in any manner. For example, thethree-dimensional regions may be determined by directly measuring thespatial locations of various points of one or more objects. Thethree-dimensional regions may also be determined by adapting a CAD modelor other computer based three dimensional model of one or more objectsthat contains position information of various locations of the objects.This approach is suitable for use with complex three-dimensionalgeometries, such as the interior of a vehicle, which are difficult orcumbersome to measure directly. Another approach is to determinelocations of points or regions of an object using one or more sensorscapable of communicating position information, such as image sensors,distance or position sensors, or the like. For example, sensors maycapture images of that object (of any wavelength, including visiblelight images, infrared images, etc.) and locations of points or regionsof the object may be determined therefrom in any manner. A furtherapproach employs one or more known machine learning models trained toselect and determine positions and orientations of regions of an objectfrom input such as images of the object.

FIG. 2B is a block diagram illustrating further details of the gazevector estimation module 220. In one embodiment, the gaze vectorestimation module 220 includes an adaptive inference fusion module 280that implements a regression model as described above. The regressionmodel takes as input variables a set of facial landmarks describingsubject head pose, a set of confidence values corresponding to thefacial landmarks, a left eye gaze direction, a right eye gaze direction,and corresponding confidence values for each eye gaze direction. Gazedirection is then output according to a regression scheme, as describedabove. The facial landmarks and associated confidence values may bedetermined according to any suitable method or system.

The eye gaze networks 260, 270 shown take as input crops of thesubject's left and right eyes, and output estimates of gaze directionsfor each eye. Eye crops may be determined by locating the subject's eyesin the image from the camera 200, and cropping the image accordingly.Eye location can be carried out in any manner, such as by known computervision-based eye detection processes including any of the abovenon-neural network based techniques, neural network-based eyerecognition methods, and the like. Eye location according to theseprocesses may generate confidence values corresponding to a degree ofcertainty that the eye has been correctly identified, and theseconfidence values may also be input to the adaptive inference fusionmodule. Eye gaze networks 260, 270 may be any network capable ofdetermining eye gaze from input eye crops.

The adaptive inference fusion module 280 may implement any regressionmodel or models suitable for determining gaze vectors, as describedabove. The gaze vector output from the adaptive inference fusion module280 is then transmitted to the gaze region mapping module 250 formapping of the gaze vector onto a three-dimensional geometry. It can beobserved that any three-dimensional geometry, or set of surfaces, may bestored for use by computing system 300. Accordingly, the system of FIGS.2A and 2B may determine the intersection of a subject's gaze with anyset of surfaces. Embodiments of the disclosure thus allow an efficientand modular approach for determining the region of gaze of a subject inany environment. By characterizing any environment as a set ofthree-dimensional surfaces and storing those surfaces in, e.g., storage408, the system of FIGS. 2A and 2B may determine which portion of theenvironment occupies the subject's attention at any time. If the subjectchanges his or her environment, any interaction with this newenvironment may be determined by inputting the surfaces of the newenvironment for use by the system of FIGS. 2A and 2B.

This system may be applied to any environment. As one example, theenvironment may be the cabin of a vehicle, and the system of FIGS. 2Aand 2B may be used to determine the portion or region of the vehicle towhich the driver is currently directing his or her attention. In thisexample, the above-described camera 200 may be installed in the vehicleto capture images of a vehicle occupant's face. Embodiments of thedisclosure may determine the gaze vector and origin point of the vehicleoccupant's gaze. Relevant portions of the vehicle's cabin may beidentified and characterized as three-dimensional surfaces, as shown inFIG. 2A. These surfaces may include, for instance, left and right frontwindshields, left and right exteriors (e.g., side windows), thevehicle's information cluster, and the vehicle's entertainment center.As above, the system of FIGS. 2A and 2B may then determine which ofthese surfaces, if any, that the determined gaze vector intersects, andtake one or more actions accordingly. For instance, upon determiningthat the vehicle occupant is the driver of the vehicle and determiningthat the driver is looking toward the information cluster, the vehiclemay project certain important information or warnings onto theinformation cluster, or highlight certain readings or indicators. Asanother example, upon determining that the driver has been looking at aregion other than the left front windshield for more than a thresholdamount of time, the vehicle may initiate a warning to the driver tofocus on the road. Embodiments of the disclosure contemplate any actionsinitiated in response to determined gaze region.

FIG. 3 is a block diagram representation of one exemplary gazedetermination system of embodiments of the disclosure. Here, computingdevice 300, which may be any electronic computing device containingprocessing circuitry capable of carrying out the gaze determination andmapping operations of embodiments of the disclosure, is in electroniccommunication with both a camera 310 and a gaze-assisted system 320. Inoperation, camera 310, which may correspond to camera 200 of FIG. 2A,captures and transmits images of a subject to computing device 300,which then implements the machine learning models of, e.g., FIGS. 2A-2B,determining from the image of camera 310 an output gaze vector anddetermining its intersection with a particular spatial region. Thecomputing device 300 transmits this intersection information togaze-assisted system 320, which takes an action or performs one or moreoperations in response.

Gaze-assisted system 320 may be any system capable of performing one ormore actions based on the spatial region intersection information itreceives from computing device 300, such as initiating operations ofsystems that correspond to the spatial regions intersected. Anyconfigurations of camera 310, computing device 300, and gaze-assistedsystem 320 are contemplated. As one example, the gaze-assisted system320 may be an autonomous vehicle capable of determining and reacting tothe gaze direction of the driver or another passenger, such as theautonomous vehicle of FIGS. 4A-4D described further below. In thisexample, camera 310 and computing device 300 may be positioned withinthe vehicle, while the gaze-assisted system 320 may represent thevehicle itself. Camera 310 may be positioned at any location within thevehicle that allows it a view of the driver or passenger. Accordingly,camera 310 may capture images of the driver and transmit them tocomputing device 300, which calculates corresponding subject gazevectors and determines their intersections with spatial regionscorresponding to portions of the vehicle. This intersection informationmay then be transmitted to, for example, another software module thatdetermines actions the vehicle may take in response. For instance, thevehicle may determine that the gaze direction intersects a side window,thus representing a distracted driver or a driver that is not payingattention to the road, and may initiate any type of operation inresponse. Such operations may include any type of warning issued to thedriver (e.g., a visual or audible warning, a warning on a heads-updisplay, or the like), auto-pilot initiation, a braking or turningoperation, or any other action. Computing device 300 may be positionedwithin the vehicle of gaze-assisted system 320 as a local processor, ormay be a remote processor that receives images from camera 310 andtransmits intersection information or instructions wirelessly to thevehicle of gaze-assisted system 320.

As another example, gaze-assisted system 320 may be a virtual reality oraugmented reality system capable of displaying images responsive tomotion and gaze of users. In this example, gaze-assisted system 320includes a virtual reality or augmented reality display, such as aheadset worn by a user and configured to project images thereto. Camera310 and computing device 300 may be positioned within the headset, withcamera 310 capturing images of the eyes of the user and computing device300 determining landmark and confidence values, as well as his or hergaze direction therefrom. This gaze direction may then be projected ontoa set of retrieved spatial regions within the virtual environment, andthe system 320 may take various actions based on the specific spatialregions upon which the user may be gazing. For example, the spatialregions may represent virtual objects that may respond to the user'sgaze, such as heads-up display regions that may display information tothe user when the user is gazing upon them. As with the autonomousvehicle example above, computing device 300 of a virtual reality oraugmented reality system may be located within system 320, e.g., withinthe headset itself, or may be located remotely so that images aretransmitted wirelessly to computing device 300 and calculated gazedirections may be transmitted wirelessly back to the headset, which inturn may perform various operations in response.

As yet another example, gaze-assisted system 320 may be a computer-basedadvertising system that determines which ads a user is looking at. Morespecifically, gaze-assisted system 320 may be any electronic computingsystem or device, such as a desktop computer, a laptop computer, asmartphone, a server computer, or the like. Camera 310 and computingdevice 300 may be incorporated into this system so that camera 310detects the user when he or she is gazing into or proximate to thedisplay of the computing device. Camera 310 may capture images of theuser and computing device 300 may determine his or her gaze directiontherefrom. Determined gaze directions can then be transmitted togaze-assisted system 320, e.g., computing device 300 displayingadvertisements for the user, a remote computing device, or the like. Thecomputing device 300 may then retrieve stored spatial regions, whereeach region may correspond to a particular portion of the display ofsystem 320. The calculated gaze direction may then be used to determinewhich region the gaze vector intersects, i.e., which ad the user isfocusing on, providing information on the effectiveness of various ads.

Gaze-assisted system 320 may further act as a user interface system forcontrolling any computing system. As above, spatial regionscorresponding to the display of a computing device may be used todetermine a user's gaze upon any regions of a displayed computingoutput. In this manner, system 320 may function as a graphical or visualuser interface system similar to a computer mouse or touchpad, wherebythe user may move a cursor and select items according to the location atwhich he or she is gazing. That is, users may move a cursor or otheritem selection icon by gazing at different areas of displayedinformation. Users may also use their gaze to select items (such as bygazing at corresponding spatial regions for more than a predeterminedperiod of time), select/press buttons, and perform any other user inputsto a computing system. Embodiments of the disclosure contemplate use ofany stored spatial regions arranged according to regions of anydisplayed computing output, for selecting portions of computing outputaccording to user gaze direction.

FIG. 4A is an illustration of an example autonomous vehicle 400, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 400 (alternatively referred to herein as the “vehicle400”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,and/or another type of vehicle (e.g., that is unmanned and/or thataccommodates one or more passengers). Autonomous vehicles are generallydescribed in terms of automation levels, defined by the National HighwayTraffic Safety Administration (NHTSA), a division of the US Departmentof Transportation, and the Society of Automotive Engineers (SAE)“Taxonomy and Definitions for Terms Related to Driving AutomationSystems for On-Road Motor Vehicles” (Standard No. J3016-201806,published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep.30, 2016, and previous and future versions of this standard). Thevehicle 400 may be capable of functionality in accordance with one ormore of Level 3-Level 5 of the autonomous driving levels. For example,the vehicle 400 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending on theembodiment.

The vehicle 400 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 400 may include a propulsion system450, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 450 may be connected to a drive train of the vehicle400, which may include a transmission, to enable the propulsion of thevehicle 400. The propulsion system 450 may be controlled in response toreceiving signals from the throttle/accelerator 452.

A steering system 454, which may include a steering wheel, may be usedto steer the vehicle 400 (e.g., along a desired path or route) when thepropulsion system 450 is operating (e.g., when the vehicle is inmotion). The steering system 454 may receive signals from a steeringactuator 456. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 446 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 448 and/or brakesensors.

Controller(s) 436, which may include one or more CPU(s), system on chips(SoCs) 404 (FIG. 4C) and/or GPU(s), may provide signals (e.g.,representative of commands) to one or more components and/or systems ofthe vehicle 400. For example, the controller(s) may send signals tooperate the vehicle brakes via one or more brake actuators 448, tooperate the steering system 454 via one or more steering actuators 456,and/or to operate the propulsion system 450 via one or morethrottle/accelerators 452. The controller(s) 436 may include one or moreonboard (e.g., integrated) computing devices (e.g., supercomputers) thatprocess sensor signals, and output operation commands (e.g., signalsrepresenting commands) to enable autonomous driving and/or to assist ahuman driver in driving the vehicle 400. The controller(s) 436 mayinclude a first controller 436 for autonomous driving functions, asecond controller 436 for functional safety functions, a thirdcontroller 436 for artificial intelligence functionality (e.g., computervision), a fourth controller 436 for infotainment functionality, a fifthcontroller 436 for redundancy in emergency conditions, and/or othercontrollers. In some examples, a single controller 436 may handle two ormore of the above functionalities, two or more controllers 436 mayhandle a single functionality, and/or any combination thereof.

The controller(s) 436 may provide the signals for controlling one ormore components and/or systems of the vehicle 400 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 458 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 460, ultrasonic sensor(s) 462, LIDARsensor(s) 464, inertial measurement unit (IMU) sensor(s) 466 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 496, stereo camera(s) 468, wide-view camera(s) 470(e.g., fisheye cameras), infrared camera(s) 472, surround camera(s) 474(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 498,speed sensor(s) 444 (e.g., for measuring the speed of the vehicle 400),vibration sensor(s) 442, steering sensor(s) 440, brake sensor(s) 446(e.g., as part of the brake sensor system 446), and/or other sensortypes.

One or more of the controller(s) 436 may receive inputs (e.g.,represented by input data) from an instrument cluster 432 of the vehicle400 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 434, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle400. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 422 of FIG. 4C), location data(e.g., the location of the vehicle 400, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 436,etc. For example, the HMI display 434 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 400 further includes a network interface 424, which may useone or more wireless antenna(s) 426 and/or modem(s) to communicate overone or more networks. For example, the network interface 424 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 426 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 4B is an example of camera locations and fields of view for theexample autonomous vehicle 400 of FIG. 4A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle400.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 400. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom-designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that includes portions of the environmentin front of the vehicle 400 (e.g., front-facing cameras) may be used forsurround view, to help identify forward-facing paths and obstacles, aswell aid in, with the help of one or more controllers 436 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (LDW), Autonomous Cruise Control(ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 470 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.4B, there may any number of wide-view cameras 470 on the vehicle 400. Inaddition, long-range camera(s) 498 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 498 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 468 may also be included in a front-facingconfiguration. The stereo camera(s) 468 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (e.g., FPGA) and a multi-core microprocessor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 468 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 468 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that includes portions of the environmentto the side of the vehicle 400 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 474 (e.g., four surround cameras 474 asillustrated in FIG. 4B) may be positioned around the vehicle 400. Thesurround camera(s) 474 may include wide-view camera(s) 470, fisheyecamera(s), 360-degree camera(s), and/or the like. For example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 474 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround-view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 400 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 498,stereo camera(s) 468), infrared camera(s) 472, etc.), as describedherein.

Cameras with a field of view that include portions of the interior orcabin of vehicle 400 may be used to monitor one or more states ofdrivers, passengers, or objects in the cabin. Any type of camera may beused including, but not limited to, cabin camera(s) 441, which may beany type of camera described herein, and which may be placed anywhere onor in vehicle 400 that provides a view of the cabin or interior thereof.For example, cabin camera(s) 441 may be placed within or on some portionof the vehicle 400 dashboard, rear view mirror, side view mirrors,seats, or doors and oriented to capture images of any drivers,passengers, or any other object or portion of the vehicle 400.

FIG. 4C is a block diagram of an example system architecture for theexample autonomous vehicle 400 of FIG. 4A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 400 in FIG.4C is illustrated as being connected via bus 402. The bus 402 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 400 used to aid in control of various features and functionalityof the vehicle 400, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 402 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 402, this is notintended to be limiting. For example, there may be any number of busses402, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses402 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 402 may be used for collisionavoidance functionality and a second bus 402 may be used for actuationcontrol. In any example, each bus 402 may communicate with any of thecomponents of the vehicle 400, and two or more busses 402 maycommunicate with the same components. In some examples, each SoC 404,each controller 436, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle400), and may be connected to a common bus, such the CAN bus.

The vehicle 400 may include one or more controller(s) 436, such as thosedescribed herein with respect to FIG. 4A. The controller(s) 436 may beused for a variety of functions. The controller(s) 436 may be coupled toany of the various other components and systems of the vehicle 400 andmay be used for control of the vehicle 400, artificial intelligence ofthe vehicle 400, infotainment for the vehicle 400, and/or the like.

The vehicle 400 may include a system(s) on a chip (SoC) 404. The SoC 404may include CPU(s) 406, GPU(s) 408, processor(s) 410, cache(s) 412,accelerator(s) 414, data store(s) 416, and/or other components andfeatures not illustrated. The SoC(s) 404 may be used to control thevehicle 400 in a variety of platforms and systems. For example, theSoC(s) 404 may be combined in a system (e.g., the system of the vehicle400) with an HD map 422 which may obtain map refreshes and/or updatesvia a network interface 424 from one or more servers (e.g., server(s)478 of FIG. 4D).

The CPU(s) 406 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 406 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 406may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 406 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 406 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)406 to be active at any given time.

The CPU(s) 406 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 406may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 408 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 408 may be programmable and may beefficient for parallel workloads. The GPU(s) 408, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 408 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 408 may include at least eight streamingmicroprocessors. The GPU(s) 408 may use computer-based applicationprogramming interface(s) (API(s)). In addition, the GPU(s) 408 may useone or more parallel computing platforms and/or programming models(e.g., NVIDIA's CUDA).

The GPU(s) 408 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 408 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting, and the GPU(s) 408 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread-schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 408 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 408 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 408 to access the CPU(s) 406 page tables directly. Insuch examples, when the GPU(s) 408 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 406. In response, the CPU(s) 406 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 408. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 406 and the GPU(s) 408, thereby simplifying the GPU(s) 408programming and porting of applications to the GPU(s) 408.

In addition, the GPU(s) 408 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 408 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 404 may include any number of cache(s) 412, including thosedescribed herein. For example, the cache(s) 412 may include an L3 cachethat is available to both the CPU(s) 406 and the GPU(s) 408 (e.g., thatis connected to both the CPU(s) 406 and the GPU(s) 408). The cache(s)412 may include a write-back cache that may keep track of states oflines, such as by using a cache coherence protocol (e.g., MEI, MESI,MSI, etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 404 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 400—such as processingDNNs. In addition, the SoC(s) 404 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 406 and/or GPU(s) 408.

The SoC(s) 404 may include one or more accelerators 414 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 404 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 408 and to off-load some of the tasks of theGPU(s) 408 (e.g., to free up more cycles of the GPU(s) 408 forperforming other tasks). As an example, the accelerator(s) 414 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 414 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 408, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 408 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 408 and/or other accelerator(s) 414.

The accelerator(s) 414 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 406. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 414 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 414. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 404 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 414 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow. Forexample, the PVA may be used to process raw RADAR data (e.g., using a 4DFast Fourier Transform) to provide a processed RADAR signal beforeemitting the next RADAR pulse. In other examples, the PVA is used fortime of flight depth processing, by processing raw time of flight datato provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including, for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 466 output thatcorrelates with the vehicle 400 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 464 or RADAR sensor(s) 460), amongothers.

The SoC(s) 404 may include data store(s) 416 (e.g., memory). The datastore(s) 416 may be on-chip memory of the SoC(s) 404, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 416 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 416 may comprise L2 or L3 cache(s) 412. Reference to thedata store(s) 416 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 414, as described herein.

The SoC(s) 404 may include one or more processor(s) 410 (e.g., embeddedprocessors). The processor(s) 410 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 404 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 404 thermals and temperature sensors, and/ormanagement of the SoC(s) 404 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 404 may use thering-oscillators to detect temperatures of the CPU(s) 406, GPU(s) 408,and/or accelerator(s) 414. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 404 into a lower powerstate and/or put the vehicle 400 into a chauffeur to safe-stop mode(e.g., bring the vehicle 400 to a safe stop).

The processor(s) 410 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 410 may further include an always-on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always-on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 410 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 410 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 410 may further include a high dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 410 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)470, surround camera(s) 474, and/or on in-cabin monitoring camerasensors. An in-cabin monitoring camera sensor is preferably monitored bya neural network running on another instance of the advanced SoC,configured to identify in-cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 408 is not required tocontinuously render new surfaces. Even when the GPU(s) 408 is powered onand actively performing 3D rendering, the video image compositor may beused to offload the GPU(s) 408 to improve performance andresponsiveness.

The SoC(s) 404 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 404 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role. The SoC(s) 404 may further include a broad range ofperipheral interfaces to enable communication with peripherals, audiocodecs, power management, and/or other devices. The SoC(s) 404 may beused to process data from cameras (e.g., connected over GigabitMultimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s)464, RADAR sensor(s) 460, etc. that may be connected over Ethernet),data from bus 402 (e.g., speed of vehicle 400, steering wheel position,etc.), data from GNSS sensor(s) 458 (e.g., connected over Ethernet orCAN bus). The SoC(s) 404 may further include dedicated high-performancemass storage controllers that may include their own DMA engines, andthat may be used to free the CPU(s) 406 from routine data managementtasks.

The SoC(s) 404 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 404 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 414, when combined with the CPU(s) 406, the GPU(s) 408,and the data store(s) 416, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 420) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and provide semanticunderstanding of the sign, and to pass that semantic understanding tothe path-planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path-planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 408.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 400. The always-onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 404 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 496 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, which usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 404 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)458. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 462, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 418 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 404 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 418 may include an X86 processor,for example. The CPU(s) 418 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 404, and/or monitoring the statusand health of the controller(s) 436 and/or infotainment SoC 430, forexample.

The vehicle 400 may include a GPU(s) 420 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 404 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 420 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 400.

The vehicle 400 may further include the network interface 424 which mayinclude one or more wireless antennas 426 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 424 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 478 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 400information about vehicles in proximity to the vehicle 400 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 400).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 400.

The network interface 424 may include an SoC that provides modulationand demodulation functionality and enables the controller(s) 436 tocommunicate over wireless networks. The network interface 424 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols. The vehicle 400 may further include data store(s)428, which may include off-chip (e.g., off the SoC(s) 404) storage. Thedata store(s) 428 may include one or more storage elements includingRAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other components and/ordevices that may store at least one bit of data.

The vehicle 400 may further include GNSS sensor(s) 458 (e.g., GPS and/orassisted GPS sensors), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)458 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to serial (RS-232) bridge. Thevehicle 400 may further include RADAR sensor(s) 460. The RADAR sensor(s)460 may be used by the vehicle 400 for long-range vehicle detection,even in darkness and/or severe weather conditions. RADAR functionalsafety levels may be ASIL B. The RADAR sensor(s) 460 may use the CANand/or the bus 402 (e.g., to transmit data generated by the RADARsensor(s) 460) for control and to access object tracking data, withaccess to Ethernet to access raw data, in some examples. A wide varietyof RADAR sensor types may be used. For example, and without limitation,the RADAR sensor(s) 460 may be suitable for front, rear, and side RADARuse. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 460 may include different configurations, such aslong-range with narrow field of view, short-range with wide field ofview, short-range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 460may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the surrounding of the vehicle 400 at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 400 lane.

Mid-range RADAR systems may include, as an example, a range of up to 460m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 450 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor system may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 400 may further include ultrasonic sensor(s) 462. Theultrasonic sensor(s) 462, which may be positioned at the front, back,and/or the sides of the vehicle 400, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 462 may be used, and different ultrasonic sensor(s) 462 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 462 may operate at functional safety levels of ASILB.

The vehicle 400 may include LIDAR sensor(s) 464. The LIDAR sensor(s) 464may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 464 maybe functional safety level ASIL B. In some examples, the vehicle 400 mayinclude multiple LIDAR sensors 464 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 464 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 464 may have an advertised rangeof approximately 100 m, with an accuracy of 2 cm-3 cm, and with supportfor a 100 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 464 may be used. In such examples,the LIDAR sensor(s) 464 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 400.The LIDAR sensor(s) 464, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)464 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 400. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a fivenanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)464 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 466. The IMU sensor(s) 466may be located at a center of the rear axle of the vehicle 400, in someexamples. The IMU sensor(s) 466 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 466 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 466 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 466 may be implemented as aminiature, high-performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 466 may enable the vehicle 400to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 466. In some examples, the IMU sensor(s) 466 and theGNSS sensor(s) 458 may be combined in a single integrated unit.

The vehicle may include microphone(s) 496 placed in and/or around thevehicle 400. The microphone(s) 496 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 468, wide-view camera(s) 470, infrared camera(s) 472,surround camera(s) 474, long-range and/or mid-range camera(s) 498,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 400. The types of cameras useddepends on the embodiments and requirements for the vehicle 400, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 400. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 4A and FIG. 4B.

The vehicle 400 may further include vibration sensor(s) 442. Thevibration sensor(s) 442 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 442 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 400 may include an ADAS system 438. The ADAS system 438 mayinclude an SoC, in some examples. The ADAS system 438 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 460, LIDAR sensor(s) 464, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 400 and automatically adjusts thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 400 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LC and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 424 and/or the wireless antenna(s) 426 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication links. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 400), while the I2V communication concept providesinformation about traffic farther ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 400, CACC may be more reliable, and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 460, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle400 crosses lane markings. An LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 400 if the vehicle 400 starts toexit the lane.

BSW systems detect and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 400 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 460, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results, whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 400, the vehicle 400itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 436 or a second controller 436). For example, in someembodiments, the ADAS system 438 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 438may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output canbe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 404.

In other examples, ADAS system 438 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity make the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware used by the primary computer is not causing material error.

In some examples, the output of the ADAS system 438 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 438indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork that is trained and thus reduces the risk of false positives, asdescribed herein.

The vehicle 400 may further include the infotainment SoC 430 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as an SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 430 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle-relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 400. For example, the infotainment SoC 430 may include radios,disk players, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands-free voice control, a heads-up display (HUD), anHMI display 434, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 430 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 438,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 430 may include GPU functionality. The infotainmentSoC 430 may communicate over the bus 402 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 400. Insome examples, the infotainment SoC 430 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 436(e.g., the primary and/or backup computers of the vehicle 400) fail. Insuch an example, the infotainment SoC 430 may put the vehicle 400 into achauffeur to safe-stop mode, as described herein.

The vehicle 400 may further include an instrument cluster 432 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 432 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 432 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 430 and theinstrument cluster 432. In other words, the instrument cluster 432 maybe included as part of the infotainment SoC 430, or vice versa.

FIG. 4D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 400 of FIG. 4A, inaccordance with some embodiments of the present disclosure. The system476 may include server(s) 478, network(s) 490, and vehicles, includingthe vehicle 400. The server(s) 478 may include a plurality of GPUs484(A)-484(H) (collectively referred to herein as GPUs 484), PCIeswitches 482(A)-482(H) (collectively referred to herein as PCIe switches482), and/or CPUs 480(A)-480(B) (collectively referred to herein as CPUs480). The GPUs 484, the CPUs 480, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 488 developed by NVIDIA and/orPCIe connections 486. In some examples, the GPUs 484 are connected viaNVLink and/or NVSwitch SoC and the GPUs 484 and the PCIe switches 482are connected via PCIe interconnects. Although eight GPUs 484, two CPUs480, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 478 mayinclude any number of GPUs 484, CPUs 480, and/or PCIe switches. Forexample, the server(s) 478 may each include eight, sixteen, thirty-two,and/or more GPUs 484.

The server(s) 478 may receive, over the network(s) 490 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced roadwork. Theserver(s) 478 may transmit, over the network(s) 490 and to the vehicles,neural networks 492, updated neural networks 492, and/or map information494, including information regarding traffic and road conditions. Theupdates to the map information 494 may include updates for the HD map422, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 492, the updated neural networks 492, and/or the mapinformation 494 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 478 and/or other servers).

The server(s) 478 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training, selflearning, reinforcement learning, federated learning, transfer learning,feature learning (including principal component and cluster analyses),multi-linear subspace learning, manifold learning, representationlearning (including spare dictionary learning), rule-based machinelearning, anomaly detection, and any variants or combinations therefor.Once the machine learning models are trained, the machine learningmodels may be used by the vehicles (e.g., transmitted to the vehiclesover the network(s) 490, and/or the machine learning models may be usedby the server(s) 478 to remotely monitor the vehicles.

In some examples, the server(s) 478 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 478 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 484, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 478 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 478 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 400. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 400, suchas a sequence of images and/or objects that the vehicle 400 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 400 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 400 is malfunctioning, the server(s) 478 may transmit asignal to the vehicle 400 instructing a fail-safe computer of thevehicle 400 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 478 may include the GPU(s) 484 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

FIG. 5 is a block diagram of an example computing device(s) 500 suitablefor use in implementing some embodiments of the present disclosure.Computing device 500 may include an interconnect system 502 thatdirectly or indirectly couples the following devices: memory 504, one ormore central processing units (CPUs) 506, one or more graphicsprocessing units (GPUs) 508, a communication interface 510, I/O ports512, input/output components 514, a power supply 516, one or morepresentation components 518 (e.g., display(s)), and one or more logicunits 520.

Although the various blocks of FIG. 5 are shown as connected via theinterconnect system 502 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 518, such as a display device, may be consideredan I/O component 514 (e.g., if the display is a touch screen). Asanother example, the CPUs 506 and/or GPUs 508 may include memory (e.g.,the memory 504 may be representative of a storage device in addition tothe memory of the GPUs 508, the CPUs 506, and/or other components). Inother words, the computing device of FIG. 5 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” “augmented reality system,” and/orother device or system types, as all are contemplated within the scopeof the computing device of FIG. 5 .

The interconnect system 502 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 502 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 506 may be directly connectedto the memory 504. Further, the CPU 506 may be directly connected to theGPU 508. Where there is direct, or point-to-point, connection betweencomponents, the interconnect system 502 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 500.

The memory 504 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 500. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 504 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium that may be used to storethe desired information and that may be accessed by computing device500. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 506 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 500 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 506 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 506 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 500 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 500, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 500 mayinclude one or more CPUs 506 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 506, the GPU(s) 508 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device500 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 508 may be an integrated GPU (e.g.,with one or more of the CPU(s) 506 and/or one or more of the GPU(s) 508may be a discrete GPU. In embodiments, one or more of the GPU(s) 508 maybe a coprocessor of one or more of the CPU(s) 506. The GPU(s) 508 may beused by the computing device 500 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 508 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 508may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 508may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 506 received via ahost interface). The GPU(s) 508 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory504. The GPU(s) 508 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 508 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 506 and/or the GPU(s)508, the logic unit(s) 520 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 500 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 506, the GPU(s)508, and/or the logic unit(s) 520 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 520 may be part of and/or integrated in one ormore of the CPU(s) 506 and/or the GPU(s) 508 and/or one or more of thelogic units 520 may be discrete components or otherwise external to theCPU(s) 506 and/or the GPU(s) 508. In embodiments, one or more of thelogic units 520 may be a coprocessor of one or more of the CPU(s) 506and/or one or more of the GPU(s) 508.

Examples of the logic unit(s) 520 include one or more processing coresand/or components thereof, such as Tensor Cores (TCs), Tensor ProcessingUnits (TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs),Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs),Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), ArtificialIntelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs),Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits(ASICs), Floating Point Units (FPUs), I/O elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The communication interface 510 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 500to communicate with other computing devices via an electroniccommunication network, including wired and/or wireless communications.The communication interface 510 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet.

The I/O ports 512 may enable the computing device 500 to be logicallycoupled to other devices including the I/O components 514, thepresentation component(s) 518, and/or other components, some of whichmay be built into (e.g., integrated in) the computing device 500.Illustrative I/O components 514 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 514 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 500. Thecomputing device 500 may include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 500 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 500 to render immersive augmented reality or virtual reality.

The power supply 516 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 516 may providepower to the computing device 500 to enable the components of thecomputing device 500 to operate.

The presentation component(s) 518 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 518 may receivedata from other components (e.g., the GPU(s) 508, the CPU(s) 506, etc.),and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to codes that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

FIG. 6 illustrates training and deployment of a machine learning modelof embodiments of the disclosure. In at least one embodiment, themachine learning model may include a neural network such as a CNN. Anuntrained neural network 606 is trained using a training dataset 602which, in some embodiments of the disclosure may be a set of images ofsubjects assuming various head poses. In at least one embodiment,training framework 604 is a PyTorch framework, whereas in otherembodiments, training framework 604 is a TensorFlow, Boost, Caffe,Microsoft Cognitive Toolkit/CNTK, MXNet, Chainer, Keras, Deeplearning4j,or other training framework. Training framework 604 trains an untrainedneural network 606 using processing resources described herein, togenerate a trained neural network 608. In at least one embodiment,initial weights may be chosen randomly or by pre-training using a deepbelief network. Training may be performed in either a supervised,partially supervised, or unsupervised manner.

In at least one embodiment, such as when a regression classifier isused, untrained neural network 606 may be trained using supervisedlearning, wherein training dataset 602 includes an input paired with adesired output, or where training dataset 602 includes input havingknown output and outputs of neural networks are manually graded. In atleast one embodiment, untrained neural network 606 is trained in asupervised manner. Training framework 604 processes inputs from trainingdataset 602 and compares resulting outputs against a set of expected ordesired outputs. In at least one embodiment, errors are then propagatedback through untrained neural network 606. Training framework 604adjusts weights that control untrained neural network 606. Trainingframework 604 may include tools to monitor how well untrained neuralnetwork 606 is converging towards a model, such as trained neuralnetwork 608, suitable to generating correct answers, such as in result614, based on known input data, such as new data 612. In at least oneembodiment, training framework 604 trains untrained neural network 606repeatedly while adjusting weights to refine an output of untrainedneural network 606 using a loss function and adjustment process, such asstochastic gradient descent. In at least one embodiment, trainingframework 604 trains untrained neural network 606 until untrained neuralnetwork 606 achieves a desired accuracy. Trained neural network 608 canthen be deployed to implement any number of machine learning operations.

In at least one embodiment, untrained neural network 606 may be trainedusing unsupervised learning, wherein untrained neural network 606attempts to train itself using unlabeled data. In at least oneembodiment, unsupervised learning training dataset 602 may include inputdata without any associated output data or “ground truth” data.Untrained neural network 606 can learn groupings within training dataset602 and can determine how individual inputs are related to untraineddataset 602. In at least one embodiment, unsupervised training can beused to generate a self-organizing map, which is a type of trainedneural network 608 capable of performing operations useful in reducingdimensionality of new data 612. Unsupervised training can also be usedto perform anomaly detection, which allows identification of data pointsin a new dataset 612 that deviate from normal or existing patterns ofnew dataset 612.

In at least one embodiment, semi-supervised learning may be used, whichis a technique in which training dataset 602 includes a mix of labeledand unlabeled data. Training framework 604 may thus be used to performincremental learning, such as through transferred learning techniques.Such incremental learning enables trained neural network 608 to adapt tonew data 612 without forgetting knowledge instilled within the networkduring initial training.

FIG. 7 is a flowchart illustrating process steps for determining gazedirection and mapping this gaze direction to regions of anythree-dimensional geometry, in accordance with embodiments of thedisclosure. The process of FIG. 7 may begin with the computing device300 receiving the set of three-dimensional surfaces corresponding to theenvironment surrounding the subject (Step 700). The computing device 300also receives an image of the subject taken by the camera (Step 710).Computing device 300 then identifies the face and eyes of the subject inthe received image, and determines facial landmark values, associatedconfidence values, and eye crops (Step 720) as above. These quantitiesare then used as input variables of a regression-based estimation of thegaze vector (Step 730) by the adaptive inference fusion module 280 ofFIG. 2B, as well as inputs to the gaze origin estimation module 240 ofFIG. 2A for determining the origin point of the gaze vector (Step 740).As above, gaze origin is determined from facial landmarks in particular.

Once the gaze vector and its origin point are determined, the mappingmodule 250 of FIG. 2A determines the intersection of the gaze vector, ifany, with the three-dimensional surfaces of Step 700 (Step 750). Thesurface or region intersected by the gaze vector is then output, and anyresponsive operation may be initiated (Step 760).

It is noted that systems and processes of embodiments of the disclosuremay be employed to determine the intersection of gaze with surfaces bothin/on an object, and external to an object. In particular, thethree-dimensional surfaces imported to mapping module 250 may includesurfaces of an object as well as surfaces external to or remote fromthat object, and mapping module 250 may determine the intersection ofgaze vectors with both surfaces of the object and surfaces remotetherefrom. For example, sets of three-dimensional surfaces may includesurfaces of a vehicle interior and objects external to the vehicle suchas stop signs, traffic lights, simulated pedestrians, or the like.Mapping module 250 may then determine both the vehicle window throughwhich the driver is gazing, and whether or not the driver is gazing at aparticular object such as a stop sign. To that end, sensors of a vehicle(e.g., cameras or other image sensors, Light Detection and Ranging(LIDAR) sensors, other remote sensing devices, or the like) maydetermine the positions and shapes of objects near the vehicle.Processors of the vehicle may then convert this sensor output tothree-dimensional surfaces in the same coordinate system as the storedthree-dimensional vehicle surfaces, and store them as additionalsurfaces of the three-dimensional surface set. The mapping module 250may then determine both the intersection of calculated gaze vectors withboth surfaces of the vehicle and any stored surfaces of objects externalto the vehicle. In this manner, systems may determine, for example,whether drivers are aware of, e.g., gazing in the direction of, variouspotential road hazards or other items that drivers should be payingattention to.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the disclosure.However, it will be apparent to one skilled in the art that the specificdetails are not required to practice the methods and systems of thedisclosure. Thus, the foregoing descriptions of specific embodiments ofthe present invention are presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many modifications andvariations are possible in view of the above teachings. For example,facial landmarks and confidence values may be determined in any manner,and gaze vector may be determined using any regression technique orother suitable approach. Additionally, embodiments of the disclosurecontemplate use of any three-dimensional surfaces or volumes, determinedand characterized in any manner, for determining intersections of gazevectors therewith. The embodiments were chosen and described in order tobest explain the principles of the invention and its practicalapplications, to thereby enable others skilled in the art to bestutilize the methods and systems of the disclosure and variousembodiments with various modifications as are suited to the particularuse contemplated. Additionally, different features of the variousembodiments, disclosed or otherwise, can be mixed and matched orotherwise combined so as to create further embodiments contemplated bythe disclosure.

What is claimed is:
 1. A method comprising: determining a gaze directionof a subject, the gaze direction determined according to output of amachine learning model having as input one or more features determinedfrom image data representing an image of the subject; retrieving one ormore spatial regions corresponding to one or more fields of view from aposition of the subject, a spatial region of the one or more spatialregions representing a three-dimensional (3D) surface corresponding toan object; determining, based at least on the gaze direction, that agaze of the subject intersects with at least the spatial region of theone or more spatial regions; and initiating an operation based at leaston the gaze of the subject intersecting with the spatial region.
 2. Themethod of claim 1, wherein the machine learning model comprises aregression model.
 3. The method of claim 1, wherein a second spatialregion of the one or more spatial regions represents a 3D surfacecorresponding to a second object.
 4. The method of claim 1, wherein theobject is a component of a vehicle.
 5. The method of claim 1, whereinthe object is within an interior of a vehicle.
 6. The method of claim 1,wherein the machine learning model further has as input at least one of:one or more of landmark points of a face of the subject, a head pose ofthe subject, one or more eye crops of the image of the subject; at leastone eye gaze direction of the subject, or a confidence value of at leastone eye gaze direction of the subject.
 7. The method of claim 1, furthercomprising determining a gaze origin, wherein the determining that thegaze of the subject intersects with the spatial region is further basedat least on the gaze origin.
 8. The method of claim 1, furthercomprising receiving the image data generated using a sensor.
 9. Themethod of claim 1, wherein the initiating comprises initiating theoperation associated with the object of a vehicle.
 10. The method ofclaim 1, wherein the determining that the gaze of the subject intersectswith the spatial region comprises: projecting the gaze direction ontothe spatial region; and determining, based at least on the projecting,that the gaze of the subject intersects with the spatial region.
 11. Themethod of claim 1, wherein the one or more spatial regions are one ormore first spatial regions corresponding to a first field of view fromthe position of the subject, and wherein the method further comprises:retrieving one or more second spatial regions corresponding to a secondfield of view from the position of the subject; determining, based atleast on the gaze direction, that the gaze of the subject intersectswith a second spatial region of the one or more second spatial regions;and initiating a second operation based at least on the gaze of thesubject intersecting with the second spatial region.
 12. The method ofclaim 1, wherein the one or more spatial regions are determined based atleast on one or more of a computer based model of one or more objects, ameasurement of the one or more objects, one or more images of the one ormore objects, or a machine learning model trained to determine one ormore positions of the one or more objects.
 13. The method of claim 1,wherein the output of the machine learning model comprises a gazevector, the gaze direction being determined based at least on the gazevector.
 14. A system comprising: one or more processing units to:determine a gaze direction of a subject, the gaze direction determinedaccording to output of a machine learning model having as input one ormore features determined from image data representing an image of thesubject; retrieve one or more spatial regions corresponding to one ormore fields of view from a position of the subject, a spatial region ofthe one or more spatial regions representing a three-dimensional (3D)surface corresponding to an object; determine, based at least on thegaze direction, that a gaze of the subject intersects with at least thespatial region of the one or more spatial regions; and initiate anoperation based at least on the gaze of the subject intersecting withthe spatial region.
 15. The system of claim 14, wherein the machinelearning model comprises a regression model.
 16. The system of claim 14,wherein a second spatial region of the one or more spatial regionsrepresents a 3D surface corresponding to a second object.
 17. The systemof claim 14, wherein the object is within an interior of a vehicle. 18.The system of claim 14, wherein the machine learning model further hasas input at least one of: one or more of landmark points of a face ofthe subject, a head pose of the subject, one or more eye crops of theimage of the subject; at least one eye gaze direction of the subject, ora confidence value of at least one eye gaze direction of the subject.19. The system of claim 14, wherein the one or more processing units arefurther to determine a gaze origin, and wherein the determining that thegaze of the subject intersects with the spatial region is further basedat least on the gaze origin.
 20. The system of claim 14, wherein theinitiating comprises initiating the operation associated with the objectof a vehicle.
 21. The system of claim 14, wherein the determining thatthe gaze of the subject intersects with the spatial region comprises:projecting the gaze direction onto the spatial region; and determining,based at least on the projecting, that the gaze of the subjectintersects with the spatial region.
 22. The system of claim 14, whereinthe one or more spatial regions are one or more first spatial regionscorresponding to a first field of view from the position of the subject,and wherein the one or more processing units are further to: retrieveone or more second spatial regions corresponding to a second field ofview from the position of the subject; determine, based at least on thegaze direction, that the gaze of the subject intersects with a secondspatial region of the one or more second spatial regions; and initiate asecond operation based at least on the gaze of the subject interestingwith the second spatial region.
 23. The system of claim 14, wherein theone or more spatial regions are determined based at least on one or moreof a computer based model of one or more objects, a measurement of theone or more objects, one or more images of the one or more objects, or amachine learning model trained to determine one or more positions of theone or more objects.
 24. The system of claim 14, wherein the output ofthe machine learning model comprises a gaze vector, the gaze directionbeing determined based at least on the gaze vector.
 25. The system ofclaim 14, wherein the system is comprised in at least one of: a controlsystem for an autonomous machine; a perception system for an autonomousmachine; a system for performing simulation operations; a system forgenerating or presenting at least one of virtual reality content oraugmented reality content; a system for performing deep learningoperations; a system implemented at least partially in a data center; ora system implemented at least partially using cloud computing resources.26. A processor comprising: one or more processing units to: determine agaze direction associated with a subject, the gaze direction determinedaccording to output of a machine learning model having as input one ormore features determined from image data representing an image of thesubject; retrieve one or more spatial regions corresponding to one ormore fields of view from a position of the subject, a spatial region ofthe one or more spatial regions representing a three-dimensional (3D)surface corresponding to an object; determine, based at least on thegaze direction associated with the subject, that a gaze of the subjectintersects with the spatial region of the one or more spatial regions;and initiate an operation based at least on the gaze of the subjectintersecting with the spatial region.